Graph-Based User Behavior Modeling: From Prediction to Fraud Detection

    ACM Knowledge Discovery and Data Mining, 2015.

    Cited by: 14|Bibtex|Views13|Links
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    Defense Advanced Research Projects Agencyusers’ preferencelatent factornormal userAnomalous BehaviorMore(13+)
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    How can we model users’ preferences? How do anomalies, fraud, and spam effect our models of normal users? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and ...

    Abstract:

    How can we model users' preferences? How do anomalies, fraud, and spam effect our models of normal users? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection. In particular, we will focus on the applica...More

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    Introduction
    • In this tutorial the authors focus on understanding anomaly and fraud detection through the lens of normal user behavior modeling.
    • Fraud, and spam effect the models of normal users?
    • In this tutorial the authors will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection.
    Highlights
    • How can we model users’ preferences? How do anomalies, fraud, and spam effect our models of normal users? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection
    • In this tutorial we focus on understanding anomaly and fraud detection through the lens of normal user behavior modeling
    • Alex Beutel is a fifth year Ph.D. candidate at Carnegie Mellon University in the Computer Science Department. He previously received his B.S. from Duke University. His Ph.D. research focuses on large scale user behavior modeling, covering both recommendation systems and fraud detection systems
    Results
    • The authors will give examples of recent research using the techniques to model, understand and predict normal behavior.
    • With this intuition for how these methods are applied to graphs and user behavior, the authors will focus on state-of-the-art research showing how the outcomes of these methods are effected by fraud, and how they have been used to catch fraudsters.
    • The data mining and machine learning communities have developed a plethora of models and methods for understanding user behavior.
    • This tutorial is aimed at anyone interested in modeling and understanding user behavior, from data mining and machine learning researchers to practitioners from industry and government.
    • For those having worked in fraud detection systems, the authors hope to inspire new research directions through connecting with recent developments in modeling “normal” behavior.
    • His Ph.D. research focuses on large scale user behavior modeling, covering both recommendation systems and fraud detection systems.
    • She received her Ph.D. from the Computer Science Department at Carnegie Mellon University in 2012.
    Conclusion
    • Her research interests span a wide range of data mining and machine learning topics with a focus on algorithmic problems arising in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection.
    • More details can be found at http://www.cs.stonybrook.edu/~leman.
    • More details can be found at http://www.cs.cmu.edu/~christos/.
    Summary
    • In this tutorial the authors focus on understanding anomaly and fraud detection through the lens of normal user behavior modeling.
    • Fraud, and spam effect the models of normal users?
    • In this tutorial the authors will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection.
    • The authors will give examples of recent research using the techniques to model, understand and predict normal behavior.
    • With this intuition for how these methods are applied to graphs and user behavior, the authors will focus on state-of-the-art research showing how the outcomes of these methods are effected by fraud, and how they have been used to catch fraudsters.
    • The data mining and machine learning communities have developed a plethora of models and methods for understanding user behavior.
    • This tutorial is aimed at anyone interested in modeling and understanding user behavior, from data mining and machine learning researchers to practitioners from industry and government.
    • For those having worked in fraud detection systems, the authors hope to inspire new research directions through connecting with recent developments in modeling “normal” behavior.
    • His Ph.D. research focuses on large scale user behavior modeling, covering both recommendation systems and fraud detection systems.
    • She received her Ph.D. from the Computer Science Department at Carnegie Mellon University in 2012.
    • Her research interests span a wide range of data mining and machine learning topics with a focus on algorithmic problems arising in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection.
    • More details can be found at http://www.cs.stonybrook.edu/~leman.
    • More details can be found at http://www.cs.cmu.edu/~christos/.
    Funding
    • This material is based upon work supported by the National Science Foundation under Grant No IIS-1247489, IIS-1217559, CNS-1314632, IIS-1408924, IIS-1408287, CAREER 1452425, DGE-1252522, by the Defense Threat Reduction Agency under contract No HDTRA1-10-1-0120, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, by the U.S Army Research Office (ARO) and Defense Advanced Research Projects Agency (DARPA) under Contract Number W911NF-11-C0088, and by the ARO Young Investigator Program under Contract No W911NF-14-1-0029. This work is also partially supported by a Google Focused Research Award, a Facebook Fellowship, a Facebook Faculty Gift, an R&D grant from Northrop Grumman Aerospace Systems, and the Stony Brook University Office of Vice President for Research
    • Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding parties
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